As is known in the art, a variety of techniques are known for enhancing images of scenes which are obscured by backscattered light. For example, there are many known methods for enhancing the contrast of images in such circumstances, but the maximum improvement in the quality of the image is limited by a number of factors, as disclosed in U.S. Pat. No. 6,462,768, which is incorporated herein by reference. For example, the gain of the camera or other sensing system is set, usually by an automatic gain control, to the maximum brightness of the image. When the scattered light component is large, the transmitted terrain component becomes small in comparison with the quantization noise of the sensor. In addition, the backscattered light often has a random component that is a source of noise which is amplified by any contrast-stretching transformation implemented by the sensor. Further, in low light conditions, statistical fluctuations in the transmitted photon flux give rise to noise in the image. This noise will be amplified by any transformation that increases the range of contrasts present in the image.
Various contrast enhancement algorithms are known, for example, variance normalization or histogram equalization, see, e.g., U.S. Pat. Nos. 6,462,768, 6,982,764, 8,331,711, 5,681,112, 5,218,649, 6,876,777, 5,300,169, and 6,064,775, and U.S. Patent Publications No. 2012/0275721, all of which are incorporated herein by reference. In practice, however, such known contrast enhancement algorithms have not provided particularly good results.
The aim of image enhancement is to modify input images in such a way that the visual content contained in the image is improved with respect to a set of defined criteria. As there is no single set of criteria which can universally define an ideal enhancement, many image enhancement techniques have been proposed. The most basic of image enhancement approaches include pixel transformations such as logarithmic transformations, gamma transformations, and contrast stretching operations, which define a fixed or parametrically adjustable one-to-one mapping by which the intensity values of individual pixels are modified. Histogram equalization is an automated enhancement process which uses the histogram of the input image itself to determine the one-to-one mapping of intensity values for which an approximately uniform distribution is yielded in the enhanced result. This procedure has been further generalized to histogram matching, whereby the input histogram is matched to any defined histogram distribution. As these methods use global image properties to determine pixel transformations and apply the same transformation to each pixel in the same way regardless of local image information, they may not be appropriately applied in a local context and often times yield inadequate detail preservation or over-enhancement. Consequently, adaptive procedures, such as contrast-limited adaptive histogram equalization, have been developed to locally adapt the enhancement process based on local image features. Moreover, algorithms such as multi-scale retinex attempt to model the transfer functions of the human optical nerve, cortex, and so forth, and formulate enhancement algorithms by implementing filters which recreate these processes to model human vision. However, the way in which these approaches actually enhance, and in particular, image edges, is still unpredictable. In this sense, the approaches may be classified as indirect image enhancement algorithms, as they enhance images and generally improve image contrast without explicitly defining image contrast itself. Conversely, direct image enhancement algorithms quantitatively define a contrast measure in either a spatial or transform domain, and achieve image enhancement by increasing the measured contrast. Accordingly, direct image enhancement algorithms have been developed using contrast measures defined in the DCT, pyramidal, and wavelet transform domains. These algorithms are capable of enhancing fine local edge structures, but generally are less successful in improving global image contrasts adequately even when scale parameters are chosen appropriately. Overall, it is still observed that no single image enhancement algorithm is capable of delivering an ideal enhancement for all circumstances and practical applications.
The goal of image denoising is to remove the noise which has corrupted an image. There may be many sources of the noise itself, including the imaging devices, particularly when image signals are weak, or a noisy transmission channel. Of particular interest is the problem of removing additive white Gaussian noise from images. The basic tradeoff which exists in denoising is between the ability to effectively remove noise while also accurately preserving edges. The most basic means of Gaussian noise denoising is Gaussian filtering. However, this approach is very prone to blurring edges and fine details as it filters isotropically. Partial differential equation (PDE) based approaches such as anisotropic diffusion generalize the replace of the isotropic filter with a conduction function which smoothes the image more in non-edge regions less on edges. Total variational approaches formulate the denoising problem as a constrained optimization problem. Wavelet-based denoising approaches have also been proposed based on several means of thresholding wavelet coefficients. Despite the formulations of algorithms, there will always inevitably be some tradeoff between sufficient noise removing and accurate edge preservation.